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Abstract

Background

Environments conducive to walking may help people avoid sedentary lifestyles and associated
diseases. Recent studies developed walkability models combining several built environment
characteristics to optimally predict walking. Developing and testing such models with
the same data could lead to overestimating one's ability to predict walking in an
independent sample of the population. More accurate estimates of model fit can be
obtained by splitting a single study population into training and validation sets
(holdout approach) or through developing and evaluating models in different populations.
We used these two approaches to test whether built environment characteristics near
the home predict walking for exercise. Study participants lived in western Washington
State and were adult members of a health maintenance organization. The physical activity
data used in this study were collected by telephone interview and were selected for
their relevance to cardiovascular disease. In order to limit confounding by prior
health conditions, the sample was restricted to participants in good self-reported
health and without a documented history of cardiovascular disease.

Results

For 1,608 participants meeting the inclusion criteria, the mean age was 64 years,
90 percent were white, 37 percent had a college degree, and 62 percent of participants
reported that they walked for exercise. Single built environment characteristics,
such as residential density or connectivity, did not significantly predict walking
for exercise. Regression models using multiple built environment characteristics to
predict walking were not successful at predicting walking for exercise in an independent
population sample. In the validation set, none of the logistic models had a C-statistic
confidence interval excluding the null value of 0.5, and none of the linear models
explained more than one percent of the variance in time spent walking for exercise.
We did not detect significant differences in walking for exercise among census areas
or postal codes, which were used as proxies for neighborhoods.

Conclusion

None of the built environment characteristics significantly predicted walking for
exercise, nor did combinations of these characteristics predict walking for exercise
when tested using a holdout approach. These results reflect a lack of neighborhood-level
variation in walking for exercise for the population studied.

Background

Environments that make walking feasible and appealing have been labeled as "pedestrian-oriented"
[1] or "walkable" [2]. Such environments may help local residents to maintain active lifestyles and to
avoid health conditions for which sedentary behavior is a known risk factor, including
obesity, diabetes, cardiovascular disease, and some types of cancer [3]. Residential density, connectivity, land use mix, facilities, paths, and aesthetic
features have all been studied as predictors of walking or physical activity [1,2,4-12], but results for these studies have not been consistent. Residential density and
connectivity, for example, are associated with walking or physical activity in some
studies [13-15], but not in others [16,17]. Unexpected but significant findings have been reported as well, including more walking
or physical activity in neighborhoods with reduced access to shops [18,19], fewer physical activity facilities [20,21], or poor sidewalk conditions [22].

Discordance among studies may be due to differences in populations, disagreement between
perceptions and objective measures of the environment, or environmental measurement
at aggregate levels that mask relevant small-scale variation [1,4,9]. More specifically, individuals may respond differently to their environment depending
on their age, affluence, car ownership, physical functioning, attitudes, preferences,
or other traits. The differences between perceived neighborhood characteristics and
objectively measured characteristics are potentially problematic because being active
may change one's perceptions of the environment, making it difficult to separately
identify the effect of environmental characteristics on activity. On the other hand,
perceptions and objective characteristics may differ because the area measured through
"objective" geographic data do not reflect the environment as experienced [23,24], either because the scale is too large or because the shape is not customized to
reflect pertinent social or physical boundaries; objective is placed in quotes here
because this term has been used to indicate that the data have come from an external
source such as a government agency or commercial firm, sources which may themselves
provide imprecise or biased data.

Associations between built environment characteristics and physical activity may also
depend on the precision or nature of the physical activity measurement. It is important
to note that the built environment characteristics that have been used to assess neighborhood
walkability may influence walking as a mode of transportation [2,7,25,26]; our study, on the other hand, evaluates whether these characteristics are associated
with walking for exercise. Characteristics of the built environment are most strongly
correlated with transportation-related activities, especially walking and biking,
that occur within the environmental context of study [17,19,27-29]. However, measures of leisure-time physical activity, including walking for exercise
or recreational purposes, may also warrant attention because of the link between leisure-time
physical activity and health. In our study, walking for exercise was measured because
of the established association between regular or brisk walking and cardiovascular
health [30-33]. Prior studies of walking for exercise or other leisure-time physical activity have
provided some support for the relevance of residential density, street connectivity,
sidewalk availability, proximity to potential destinations or fitness centers and
parks for these outcomes [1,22,34-40]. However, one should note that the built environment characteristics we measured
may have public health relevance through a pathway that does not include walking for
exercise.

A final source of inconsistency among published associations of built environment
characteristics with physical activity warrants attention: multiple testing or empirically
driven model building that could inflate type I errors (false positive associations).
Even in the setting of careful measurement, the potential for false positive findings
is increased by the practice of screening numerous built environment characteristics
for positive associations, and publishing these without independent replication. One
way studies of the built environment and health might limit false positive associations
is through the creation and validation of a walkability model or index combining several
built environment characteristics to optimally predict walking [27,41-43]. The common practice of developing and testing models using the same data, however,
could lead to overestimating model fit and prediction [44]. More accurate estimates of model fit can be obtained by splitting a single study
population into training and validation sets (holdout approach) or through developing
and testing models in different populations [45]. A holdout approach involves exploration and model fitting for a random selection
of the study data, called the training set. The remaining data, called the validation
or test set, is reserved for replication of the initial results, and to estimate of
how well the model would fit an independent sample of data. False positive findings
are unlikely to be replicated in the validation set.

In this study, data for a healthy population in Washington State were analyzed using
a holdout approach. Our objective was to evaluate whether built environment characteristics
near the home could be used to predict walking for exercise. We created models using
built environment characteristics near each participant's home address to predict
walking for exercise, and then evaluated these models on a random subset of the study
data. We also evaluated models based on data from a previously described study using
a different sampling frame within the same region [27,42].

Materials and methods

Study setting and population

Data came from the Heart and Vascular Health (HVH) study, an ongoing population-based
case-control study in the Puget Sound Region of Washington State [46]. Subjects lived in King, Kitsap, Pierce, Snohomish, and Thurston counties; King County,
the most populous of these, contains the City of Seattle. Although much of the land
area included is rural, 97 percent our study population lived in non-rural areas (defined
as a residential density ≥ 96.5 units/km2 [250 units/mi2] [47]).

The HVH study was designed for investigating pharmacological and genetic influences
on cardiovascular disease, but we used data on 1,608 control participants to examine
the effects of the built environment on walking for exercise. The controls from this
study were a stratified random sample of 30 to 79 year old members of Group Health,
a large health maintenance organization serving approximately 500,000 Washington State
residents. Participants gave informed consent, and the human subjects review committee
at Group Health and the University of Washington approved all study procedures.

Only controls were included in this analysis, to limit possible recall bias or confounding
by preclinical cardiovascular disease. Participants were also excluded if they had
a documented history of myocardial infarction, stroke, congestive heart failure or
angina, or if they reported fair or poor health prior to their reference date. These
exclusions were designed to identify a healthy population in which physical activity
might be important for primary prevention of disease, while excluding those with major
health limitations that could influence both place of residence and physical activity
patterns.

We randomly assigned each participant a reference date within the year of selection
as a control (1995 to 2001). Information preceding the reference date was collected
from medical records and telephone interviews; the reference date was used by the
original study to ensure comparable data quality for myocardial infarction cases and
frequency matched controls. Telephone interviews took place from 1995 to 2004, an
average of about two years (standard deviation: 0.7 years) after the assigned reference
date; 76 percent of eligible, contacted controls agreed to participate in a telephone
interview. Compared with participants who allowed us only to examine their medical
record, participants completing the telephone interview were more likely to have treated
hypertension, treated diabetes, or a body mass index above 30 and less likely to be
residents of King County (chi-squared test p < 0.05).

Physical activity and participant characteristics

The telephone interview included questions on physical activity derived from the Minnesota
Leisure-Time Physical Activity questionnaire [48]. The Minnesota Leisure-Time Physical Activity questionnaire has a high test-retest
reliability [49] for physical activity over the last year, with one month interval between tests,
but has been modified for our study. Participants in the HVH study were asked to report
the frequency and duration of their participation in 26 types of physical activity,
including "walking for exercise", for a one-month period before their reference date.
Frequency and average duration were used to estimate the minutes per week spent walking
for exercise. Previous studies have found that data from this questionnaire on physical
activity or walking for exercise are associated with incident myocardial infarction
in this study population [46,50], which suggests the modified questionnaire has predictive validity and relevance
to cardiovascular health.

The telephone interview also included questions on the participant's race, general
health status (classified as excellent, very good, good, fair, or poor), smoking status,
employment status, education, and income. Data from Group Health medical and pharmaceutical
records were used to assess whether each participant had treated hypertension or treated
diabetes. Measured height and weight were taken from the medical record and used to
calculate body mass index (weight in kilograms/height in meters, squared). Obesity
was defined as a body mass index above 30.

Addresses and geocoding

Residential addresses were obtained from Group Health's archived end-of-year membership
files for the December before each participant's reference date. An automated process
in Maptitude software [51], version 4.7 (Caliper Corporation, Newton/MA, 2004), successfully geocoded 97 percent
of addresses, and an additional two percent were geocoded following manual cleaning
of the address data. Participants were excluded if they had no address or only a Post
Office box available (n = 79); an address that could not be geocoded (n = 4); or an
address located outside of the five-county study area (n = 72).

One-kilometer airline buffers (circles with one kilometer radius surrounding each
address) were created using ArcView 3.2 (ESRI, Redlands/CA, 1999). Airline buffers
based on Euclidean distance were used instead of network buffers based on empirical
evidence from the same geographic region [52] and the high permeability of urban environments to pedestrians [53]. One kilometer buffers were selected because of the relatively small territory typically
covered on foot [8,29] and the lack of correlation between perceived and objective measures of the built
environment beyond one kilometer [20,42].

Built environment data

For each of the five study counties digital maps of street networks, parks, and tax
parcels (defined as buildings or units of land that are taxed or exempt from taxation)
were obtained through the Washington State Geospatial Data Archive [56], county agencies, or cities (sidewalks, for King County only). Built environment
data sources used were produced between 1998 (the midpoint of the study period) and
2005; although data from 1998 were sought in all cases, more recent data were used
for several built environment characteristics because older data had not been archived,
were of poor quality, or did not exist for a given county.

Residential density was calculated as housing units per square kilometer, with a housing
unit defined as a house, apartment, mobile home, or other dwelling intended for occupancy
as separate living quarters [57]. Residential density of each one-kilometer buffer was estimated using an area-weighted
average of densities from census block groups intersecting or contained in the buffer.
For example, a subject might have 30 percent of their one-kilometer buffer in census
block group A, and 70 percent in census block group B. The estimated density for the
one-kilometer buffer would then be 0.3 * (density of A) + 0.7 * (density of B). As
a measure of connectivity, block size was calculated using local street maps. For
sidewalk availability, the total length of sidewalk-lined streets within each one-kilometer
buffer was calculated. Sidewalk data were only available for King County.

We estimated proximity to several potential walking destinations (grocery stores,
schools, restaurants and bars, banks, grocery-restaurant-retail complexes, office
complexes, school-church combinations, fitness facilities, and parks), calculating
the distance to the closest destination of each type and the number of destination
of each type within one kilometer. For the destination combinations (grocery-restaurant-retail
complexes, office complexes, and church-school combinations), the area of the nearest
one was also calculated. Park access was measured as the proportion of the one-kilometer
buffer covered by parks. With the exception of parks, which were identified using
digital maps of parks in each county, destinations were identified using tax parcel
land use codes. The categorization of the land use codes differed by county, but consistent
rules were applied to categorize land uses across counties.

Statistical analysis

Built environment characteristics were tested as predictors of walking for exercise.
All participants were included in analyses of logistic models predicting some walking
versus no walking, and those who walked were included in linear models to predict
amount of walking (average minutes per week). Time spent walking for exercise was
log-transformed to moderate the effects of skewness and heteroscedasticity.

We tested single built environment characteristics and models using multiple built
environment characteristics to predict walking. Some built environment characteristics
may be associated with walking in our sample by chance alone, raising concerns about
multiple comparisons. If we fit a model to our data, and then tested the model using
the same data, our estimates of model fit would be artificially high because any chance
associations unique to our data would be incorporated into our model. This would overestimate
our ability to predict walking in a different sample of individuals from the same
population. A holdout approach was used to avoid this bias [44,45]. Models developed in a training set were tested in a validation set, with estimates
of model fit based on the validation set considered to be more accurate.

The training set (a stratified random sample of 2/3 of participants) and validation
set (the remaining 1/3 of participants) were similar with regard to demographic, socioeconomic,
health, and built environment characteristics. The random sampling was stratified
by King County residence, because we decided a priori to separately create and evaluate models for the subset that lived in King County,
in addition to pooled models for the entire region. More than half of area residents
and a majority of our study participants (58%) lived in King County.

Built environment characteristics were modeled within categories or log-transformed
in order to reduce the influence of outliers. Proximity to destinations of each type
was categorized as within 500 m, 500 m to 1000 m, or more than 1000 m away. Density,
connectivity, sidewalk availability, and park access were log transformed. Regression
models were used to calculate the predicted probability of walking for exercise or
predicted minutes per week of walking for exercise. These predicted variables were
proportional to the linear predictors from the corresponding models: a constant (alpha)
added to the product of each built environment characteristic (x) and the corresponding
slope parameter (beta coefficient): predicted minutes/week of walking = α + Σxiβi. Slope parameters were estimated from training set data.

In addition, models were created using the Walkable and Bikeable Community (WBC) study
model components: residential density; household and average block size; sidewalk
availability; number of schools, restaurants or bars, grocery stores, and grocery-restaurant-retail
complexes; distance to the closest restaurant or bar; distance to the closest grocery
store; and area of the closest office complex [27,42]. We evaluated regression models with slope parameters for these 11 characteristics
based our study's training set or on the WBC study data [27,42] (reanalyzed with exclusions, adjustments, and regression techniques parallel to those
used for the present study).

For logistic regression models, model fit was evaluated using Hosmer-Lemeshow tests
[45] and C-statistics (based on the area under the receiver operating characteristic curve).
Under the null hypothesis, the logistic model predicts walking no better than expected
by chance, and one would expect a C-statistic of 0.5; a model with perfect prediction
would lead to a C-statistic of 1.0. Predictive utility of linear models was assessed
through the percent of variation explained: r-squared * 100 percent.

Unadjusted models were compared with models adjusted for age, sex, self-reported health
status, income, and education. For adjusted models, missing values for income (10
percent) and education (less than one percent) were estimated through multiple imputation
[58]. Because unadjusted and adjusted models were similar, we have presented the unadjusted
models in our tables. All regression models were run using robust variance estimates
in Stata 8.2 (StataCorp, College Station/TX, 2003), and variance estimates accounted
for clustering within county of residence.

Intra-class correlation coefficients (ICCs) were used to evaluate how characteristics
varied between versus within ZIP codes, census tracts, and census block groups [59]. These ICCs can be interpreted as the maximum proportion of variation explained at
the given group-level. If a characteristic was constant within each group, the only
variation would be between groups and the ICC would be 1.0. In contrast, if the characteristic
was randomly distributed with respect to group, the ICC would be close to zero. These
estimates were based on one-way analysis of variance (ANOVA) models. Continuous variables
were log-transformed to more closely meet the normality assumption of the ANOVA model.
The ANOVA ICC estimator was also used for dichotomous variables, for which the ICC
estimation remains asymptotically valid and unbiased [60].

Results

For 1,608 participants meeting the inclusion criteria, the mean age was 64 years,
61 percent were female, 90 percent were white, 37 percent had a college degree, and
46 percent were retired. The annual household income was above $50,000 for 51 percent
of non-retired participants and 21 percent of retired participants.

Sixty-two percent of participants reported that they walked for exercise (Table 1). Older participants and women were more likely to report walking for exercise. Even
after excluding participants in fair or poor health, general self-reported health
status was associated with walking. Among those who reported walking for exercise,
the median walking time was 2.3 hours per week (interquartile range: 1.4 to 3.6 hours
per week) and the mean walking time was 2.9 hours per week (standard deviation: 2.5
hours per week).

Table 1. Characteristics of participants who did and did not walk for exercise

We evaluated single built environment characteristics, including residential density,
street connectivity, sidewalk availability, proximity to destinations, and park access,
as predictors of walking for exercise. Density of housing units had a C-statistic
of 0.52 (95 percent confidence interval: 0.49, 0.55) for predicting walking versus
no walking and explained less than 0.1 percent of the variation in walking time (Table
2). Connectivity, measured by block size, had a C-statistic of 0.49 (95 percent confidence
interval: 0.46, 0.51) and explained 0.6 percent of walking time. Sidewalk availability,
measured only in King County, had a C-statistic of 0.51 (95 percent confidence interval:
0.47, 0.54) and explained 0.1 percent of the variation in walking time. Similarly
modest results were found for other single measures, such as proximity of the various
destinations (Table 2). A more general measure of proximity to potential destinations (proportion of the
one-kilometer buffer occupied by commercial buildings) was also considered, but was
not significantly associated with walking for exercise.

Table 2. Built environment characteristics used one at a time to predict walking for exercise

Built environment characteristics were then combined to create linear and logistic
models predicting walking for exercise, to be validated using a holdout approach.
Parameter estimates from logistic and linear models fitted to the training set are
shown in Table 3 (un-italicized estimates). Several built environment characteristics were significantly
associated with walking for exercise in the training set (indicated by bold text).
The training set models using this bloc of predictors were evaluated using a holdout
approach, with the corresponding measures of model fit shown at the top of Table 4.

Table 3. Models using multiple built environment characteristics to predict walking for exercise

Table 4. Holdout validation and replication of models using the built environment to predict
walking for exercise

In the training set, the logistic regression model shown in Table 3 had an overall C-statistic of 0.61 (95 percent confidence interval: 0.58, 0.65) for
predicting some walking for exercise versus none (Table 4, top). A Hosmer-Lemeshow test showed that the expected and observed numbers of walkers
were similar across deciles of predicted probability of walking, so that the logistic
regression model was not significantly rejected for the training set on the basis
of this goodness-of-fit test. In the training set, the linear regression model predicted
about four percent of the variation in walking time (Table 4, top).

In accordance with the planned holdout approach, models with parameter estimates based
on the training set were evaluated in the validation set to more accurately estimate
how well they would predict walking in a new sample of individuals from the same population.
When the logistic model with parameters based on the training set was used to predict
walking in the validation set, the C-statistic estimate had a confidence interval
that included the null value of 0.5 and the percent of variation explained by the
linear model was less than one percent (Table 4, top). In the validation set, the Hosmer-Lemeshow test indicated that the model did
not fit the data well: across deciles of predicted walking probability, the expected
and observed numbers of walkers were significantly different (p < 0.001). The pattern observed in the validation set data significantly deviated from
what was expected based on the model fitted to the training set data.

In a post hoc analysis, we created logistic and linear models with parameters based
on the validation set (Table 3, italicized estimates). While some parameters were similar for the training and validation
sets, others were significant in each model but of opposite sign. For example, sidewalk
availability was associated with a lower probability of walking in the training set
but a higher probability of walking in the validation set.

Logistic and linear models using the same bloc of built environment characteristics
were also estimated for the King County residents only or with adjustment for potential
confounders. When restricted to King County residents, estimates of model fit in the
training set were even higher (Table 4).

The models tested using the holdout approach may have failed in the validation set
because they incorporated so many variables; the number of variables increases the
probability that the model will overfit the training set data, explaining random noise
unique to the data. In order to address this concern, we repeated the process of model
fitting and model evaluation with a smaller number of variables, selected based on
their inclusion in the models from the WBC study [27,42]. When considering the 11 components of the WBC study models (Table 5), the direction of association was not consistent for these built environment characteristics
between the models in the HVH study population and those from the WBC study. Neither
models with training set parameter estimates nor those with WBC parameter estimates
significantly predicted the corresponding walking outcome outside of the sample in
which it was fitted (Table 4).

Selecting variable transformations that maximized the model fit in the training set,
adjusting for potential confounders (sex, age, health status, education and income),
or restricting to non-rural areas did not improve model fit in the validation set.
Models using the same built environment characteristics also failed to reliably predict
total physical activity time per week [61], a measure described elsewhere [46,50].

To better understand these results, the geographic variation in walking for exercise
and three continuous measures of the built environment (density, connectivity, and
park area) were explored using ICCs (Table 6). Small correlations were observed within census areas for amount of walking for
exercise but these were not significant. Since the ICC confidence intervals for both
walking measures included 0.000, the data were compatible with no neighborhood-level
pattern in walking for exercise. Residential density, connectivity, and park area
were highly correlated within census tracts and census block groups, as expected.

Table 6. Geographic variation in physical activity and the built environment

Discussion

In this study, built environment characteristics were measured within one kilometer
of participants' residential addresses, but these built environment characteristics
were not consistent predictors of walking for exercise. Models using these built environment
characteristics to predict walking for exercise could not be validated using a holdout
approach. This was true for the outcomes of walking for exercise versus not walking
for exercise or amount of walking for exercise among those who walked; for models
with parameters estimated from a random sample of the study data or parameters estimated
from a different study population in the same geographic region; for analyses restricted
to the most populous county or to non-rural areas; and for models with and without
adjustment for potential confounders. Participants living in the same census block
group, census tract, or ZIP code were no more similar with respect to walking for
exercise than would be expected by chance. This lack of significant neighborhood-level
variation in physical activity variables was found despite the presence of neighborhood-level
variation in residential density, connectivity, and park area.

This study suggests that the amount of walking for exercise explained by the objectively
measured built environment characteristics near one's home may be quite small, possibly
accounting for one percent of the total variation. The importance of immediate physical
surroundings may be limited because of the many social and psychological influences
shaping physical activity behavior [39,62,63]. The larger estimates of model fit from the training set did not reflect how well
the models would predict walking for exercise in an independent sample of the same
population.

The physical activity data used in this study were collected for their relevance to
cardiovascular disease [46,50]. Walking for transportation or walking within one's neighborhood may be more sensitive
to the local built environment, but should continue to be evaluated with respect to
health outcomes [64,65]; walking pace and validity of self-report may be lower for transportation walking
compared with walking for exercise [66] so that the association between transportation walking and improved health outcomes
should be tested and not assumed. The present findings do not directly address the
hypothesis that built environment characteristics influence walking for transportation,
which has been separately evaluated in the same geographic region [67] and elsewhere [7,14,25,68-70].

Previous studies that measured walking for different purposes found different neighborhood
determinants of walking for transportation versus recreational purposes [2,9,22,27,28,35,71,72], and the neighborhood built environment has been more strongly associated with walking
for transportation as compared with walking for exercise or recreation. While expert
consensus [39] and some previous studies [20,34,37,38,71-73] support an association between the neighborhood built environment and walking for
recreation or exercise, findings from the present study agree with studies in other
regions and populations that have reported no association between the built environment
and walking for exercise or recreation [16,21,22,28].

More than half of our study population was age 65 or older. Older adults may be particularly
sensitive to their built environment [74,75] and several studies that have focused on the importance of the built environment
for supporting the physical activity and independence of older adults. Urban design,
the availability of services, recreational facilities, and safety from crime have
been associated with more walking in previous studies of older adults [13,34,76,77]. One study of older women found stronger associations between the built environment
and pedometer measures, compared with self-reported physical activity, suggesting
transport walking may be important [77]. However, older adults may be less likely to walk for transportation [27]. Future research on the importance of walkability for older adults may find pedometer
measures to be more sensitive to the built environment. There is also some evidence
that walkability may affect the physical activity and health of older adults through
increased social capital [78] or social cohesion [79], and understanding the multiple pathways through which the built environment affects
health will be important for guiding policy decisions [67].

Limitations

Data on walking for exercise were derived from telephone interview data, a method
subject to recall error [64,80] and social desirability bias [81]. Compared with more vigorous physical activities, walking may be underestimated due
to low salience [4,80,82]. Also, the external validity of the present study is limited by the setting and by
restrictions chosen to enhance internal validity: all participants had health insurance,
participated in a telephone interview, reported good health, had no history of cardiovascular
disease, and lived in the Puget Sound Region of Washington State. While these restrictions
served to reduce confounding by socioeconomic status or prior health status, they
may also have reduced variability. This observational study cannot exclude the possibility
that uncontrolled confounding is masking the true relationship between the built environment
and walking for exercise.

The measurements of the built environment for this study were based on publicly available
data sources. Tax parcel land use codes may have misclassified some relevant destinations,
and some built environment characteristics may have changed between the time measurement
and the period of physical activity assessment. Some aspects of the local built environment
that could influence walking for exercise, such as walking trails, tree cover, landscaping,
hills, or building architecture [2,81,83], were not assessed in this study. However, the lack of geographic variation in the
outcome, walking for exercise, would limit this study's statistical power to investigate
other built environment characteristics. Finally, built environment characteristics
were assessed for one-kilometer circular buffers which may not precisely reflect the
environment experienced by study participants [23,24].

Conclusion

Built environment characteristics near home did not consistently predict walking for
exercise in this healthy population in western Washington State. Further, there was
little evidence of neighborhood-level variation in walking for exercise, despite neighborhood-level
variation in the built environment. The built environment may support walking for
other specific purposes, such as transportation. The poor prediction of walking for
exercise in our study may be due to a weak association between the built environment
and walking for exercise, or may reflect the need to incorporate a wider range of
built environments by conducting national or international studies [10]. Future research is needed to estimate and confirm the effects of the built environment
on different types and measures of walking, physical activity, and health outcomes.
Replication across study populations is needed to support accurate predictions, cost-effectiveness
analyses, intervention studies, and recommendations for health promotion.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

All authors contributed to the study design, analytic approach, and presentation of
results. The geographic information systems tools and data layers for this project
were developed by GSL, AVM, ALP, and PMH. GSL geocoded the study addresses and created
neighborhood measures, conducted the analyses, and prepared the manuscript. All authors
critically reviewed manuscript drafts and approved the final manuscript.

Acknowledgements

This research was supported by a University of Washington Royalty Research fund award;
by contracts R01-HL043201, R01-HL068639, and T32-HL07902 from the National Heart,
Lung, and Blood Institute; and by grant R01-AG09556 from the National Institute on
Aging. The authors thank the Robert Wood Johnson Foundation Health & Society Scholars
program for its financial support.

Lovasi GS: Neighborhood Walkability, Physical Activity, and Cardiovascular Risk. In Epidemiology . Volume PhD. Seattle , University of Washington, School of Public Health and Community Medicine; 2006::64.